Automatic speaker verification systems are vulnerable to spoofing attacks. Recently, various countermeasures have been developed for detecting high technology attacks such as speech synthesis and voice conversion. However, there is a wide gap in dealing with replay attacks. In this paper, we propose a new feature for replay attack detection based on single frequency filtering (SFF), which provides high temporal and spectral resolution at each instant. Single frequency filtering cepstral coefficients (SFFCC) with Gaussian mixture model classifier are used for the experimentation on the standard BTAS-2016 corpus. The previously reported best result, which is based on constant Q cepstral coefficients (CQCC) achieved a half total error rate of 0.67% on this data-set. Our proposed method outperforms the state of the art (CQCC) with a half total error rate of 0.0002%.
Cite as: Alluri, K.N.R.K.R., Achanta, S., Kadiri, S.R., Gangashetty, S.V., Vuppala, A.K. (2017) Detection of Replay Attacks Using Single Frequency Filtering Cepstral Coefficients. Proc. Interspeech 2017, 2596-2600, doi: 10.21437/Interspeech.2017-256
@inproceedings{alluri17b_interspeech,
author={K.N.R.K. Raju Alluri and Sivanand Achanta and Sudarsana Reddy Kadiri and Suryakanth V. Gangashetty and Anil Kumar Vuppala},
title={{Detection of Replay Attacks Using Single Frequency Filtering Cepstral Coefficients}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={2596--2600},
doi={10.21437/Interspeech.2017-256}
}